Theoretical Exploring Selective-Binding Mechanisms of JAK3 by 3D-QSAR, Molecular Dynamics Simulation and Free Energy Calculation

Identification of novel covalent JAK3 inhibitors through consensus scoring virtual screening: integration of common feature pharmacophore and covalent docking

Accumulated research strongly indicates that Janus kinase 3 (JAK3) is intricately involved in the initiation and advancement of a diverse range of human diseases, underscoring JAK3 as a promising target for therapeutic intervention. However, JAK3 shows significant homology with other JAK family isoforms, posing substantial challenges in the development of JAK3 inhibitors. To address these limitations, one strategy is to design selective covalent JAK3 inhibitors. Therefore, this study introduces a virtual screening approach that combines common feature pharmacophore modeling, covalent docking, and consensus scoring to identify novel inhibitors for JAK3. First, common feature pharmacophore models were constructed based on a selection of representative covalent JAK3 inhibitors. The optimal qualitative pharmacophore model proved highly effective in distinguishing active and inactive compounds. Second, 14 crystal structures of the JAK3-covalent inhibitor complex were chosen for the covalent docking studies. Following validation of the screening performance, 5TTU was identified as the most suitable candidate for screening potential JAK3 inhibitors due to its higher predictive accuracy. Finally, a virtual screening protocol based on consensus scoring was conducted, integrating pharmacophore mapping and covalent docking. This approach resulted in the discovery of multiple compounds with notable potential as effective JAK3 inhibitors. We hope that the developed virtual screening strategy will provide valuable guidance in the discovery of novel covalent JAK3 inhibitors.

A novel approach to study multi-domain motions in JAK1's activation mechanism based on energy landscape

The family of Janus Kinases (JAKs) associated with the JAK-signal transducers and activators of transcription signaling pathway plays a vital role in the regulation of various cellular processes. The conformational change of JAKs is the fundamental steps for activation, affecting multiple intracellular signaling pathways. However, the transitional process from inactive to active kinase is still a mystery. This study is aimed at investigating the electrostatic properties and transitional states of JAK1 to a fully activation to a catalytically active enzyme. To achieve this goal, structures of the inhibited/activated full-length JAK1 were modelled and the energies of JAK1 with Tyrosine Kinase (TK) domain at different positions were calculated, and Dijkstra's method was applied to find the energetically smoothest path. Through a comparison of the energetically smoothest paths of kinase inactivating P733L and S703I mutations, an evaluation of the reasons why these mutations lead to negative or positive regulation of JAK1 are provided. Our energy analysis suggests that activation of JAK1 is thermodynamically spontaneous, with the inhibition resulting from an energy barrier at the initial steps of activation, specifically the release of the TK domain from the inhibited Four-point-one, Ezrin, Radixin, Moesin-PK cavity. Overall, this work provides insights into the potential pathway for TK translocation and the activation mechanism of JAK1.

Molecular modeling strategy for detailing the primary mechanism of action of copanlisib to PI3K: combined ligand-based and target-based approach

Since dysregulation of the phosphatidylinositol 3-kinase (PI3K) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3K has emerged as an attractive target for drug development. Although copanlisib is the first pan-PI3K inhibitor to be approved for clinical use, the precise mechanism by which it acts on PI3K has not been fully elucidated. To reveal the binding mechanisms and structure-activity relationship between PI3K and copanlisib, a comprehensive modeling approach that combines 3D-quantitative structure-activity relationship (3D-QSAR), pharmacophore model, and molecular dynamics (MD) simulation was utilized. Initially, the structure-activity relationship of copanlisib and its derivatives were explored by constructing a 3D-QSAR. Then, the key chemical characteristics were identified by building common feature pharmacophore models. Finally, MD simulations were performed to elucidate the important interactions between copanlisib and different PI3K subtypes, and highlight the key residues for tight-binding inhibitors. The present study uncovered the principal mechanism of copanlisib's action on PI3K at the theoretical level, and these findings might provide guidance for the rational design of pan-PI3K inhibitors.Communicated by Ramaswamy H. Sarma.

A multitask GNN-based interpretable model for discovery of selective JAK inhibitors

The Janus kinase (JAK) family plays a pivotal role in most cytokine-mediated inflammatory and autoimmune responses via JAK/STAT signaling, and administration of JAK inhibitors is a promising therapeutic strategy for several diseases including COVID-19. However, to screen and design selective JAK inhibitors is a daunting task due to the extremely high homology among four JAK isoforms. In this study, we aimed to simultaneously predict pIC₅₀ values of compounds for all JAK subtypes by constructing an interpretable GNN multitask regression model. The final model performance was positive, with R² values of 0.96, 0.79 and 0.78 on the training, validation and test sets, respectively. Meanwhile, we calculated and visualized atom weights, followed by the rank sum tests and local mean comparisons to obtain key atoms and substructures that could be fine-tuned to design selective JAK inhibitors. Several successful case studies have demonstrated that our approach is feasible and our model could learn the interactions between proteins and small molecules well, which could provide practitioners with a novel way to discover and design JAK inhibitors with selectivity.

Theoretical study of myriocin-binding mechanism targeting serine palmitoyltransferase

Sphingolipids (SLs) are vital for cells as forming membrane and transducing signals. The first step for de novo biosynthesis of SLs is catalyzed by the pyridoxal-5'-phosphate (PLP)-dependent enzyme serine palmitoyltransferase (SPT), which has been proven to be a promising drug target for treating various diseases. However, there are few SPT-specific inhibitors have been identified so far. Myriocin, a natural fungal product, is confirmed as the most potent inhibitor of SPT and has been widely used, but studies of its molecular mechanism are still underway. Besides, there is no intact co-crystal structure of SPT-binding myriocin until now. Aiming to uncover the interaction mechanism between SPT- and PLP-binding myriocin at the molecular level, a systematic computational strategy was performed in this present study. Firstly, covalent docking was implemented to preliminarily predict the binding pose SPT/PLP-myriocin aldimine and its structurally similar intermediate SPT/PLP-β-ketoacid aldimine. Secondly, two binding complexes were treated as initial structures to perform molecular dynamics simulations and binding free energy calculations. The calculated docking scores and predicted binding energies were consistent with the reported bioactivities. Finally, the binding mechanism of myriocin binding with SPT was meticulously described, and the key residues making favorable contributions were highlighted. Taken together, the current study could provide some important information and valuable guidance for further rational screening, design, and modification of potent specific SPT inhibitors.

Network-Based Integrated Analysis of Transcriptomic Studies in Dissecting Gene Signatures for LPS-Induced Acute Lung Injury

Acute lung injury (ALI) is a type of serious clinical syndrome leading to morbidity and mortality. However, the precise pathogenesis of ALI remains elusive. Here, we implemented an integrative meta-analysis of six GEO microarray studies with 76 samples in the ALI mouse model. A total of 958 differentially expressed genes (DEGs) were identified in LPS relative to normal samples. Then, a network-based meta-analysis was used to mine core DEGs and to unfold the interactions among these genes. We found that Ebi3 was the top upregulated genes in the LPS-induced ALI. GO, KEGG, and GSEA analyses were performed for functional annotation. qRT-PCR revealed augmented expression of six candidate genes (Stat1, Syk, Jak3, Rac2, Ripk1, and Traf6) in the established ALI mouse model with LPS exposure. Taken together, our study investigated comprehensively hub DEGs and their networks for LPS-stimulated ALI, which might afford an additional approach to determine biomarkers and therapeutic targets and explore the molecular pathophysiology toward ALI.

Discovery of potential inhibitors targeting the kinase domain of polynucleotide kinase/phosphatase (PNKP): Homology modeling, virtual screening based on multiple conformations, and molecular dynamics simulation

In recent years, the level of interest has been increased in developing the DNA-repair inhibitors, to enhance the cytotoxic effects in the treatment of cancers. Polynucleotide kinase/phosphatase (PNKP) is a critical human DNA repair enzyme that repairs DNA strand breaks by catalyzing the restoration of 5'-phosphate and 3'-hydroxyl termini that are required for subsequent processing by DNA ligases and polymerases. PNKP is the only protein that repairs the 3'-hydroxyl group and 5'-phosphate group, which depicts PNKP as a potential therapeutic target. Besides, PNKP is the only DNA-repair enzyme that contains the 5'-kinase activity, therefore, targeting this kinase domain would motivate the development of novel PNKP-specific inhibitors. However, there are neither crystal structures of human PNKP nor the kinase inhibitors reported so far. Thus, in this present study, a sequential molecular docking-based virtual screening with multiple PNKP conformations integrating homology modeling, molecular dynamics simulation, and binding free energy calculation was developed to discover novel PNKP kinase inhibitors, and the top-scored molecule was finally submitted to molecular dynamics simulation to reveal the binding mechanism between the inhibitor and PNKP. Taken together, the current study could provide some guidance for the molecular docking based-virtual screening of novel PNKP kinase inhibitors.

A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ

Nowadays, more and more attention has been attracted to develop selective PI3Kγ inhibitors, but the unique structural features of PI3Kγ protein make it a very big challenge. In the present study, a virtual screening strategy based on machine learning with multiple PI3Kγ protein structures was developed to screen novel PI3Kγ inhibitors. First, six mainstream docking programs were chosen to evaluate their scoring power and screening power; CDOCKER and Glide show satisfactory reliability and accuracy against the PI3Kγ system. Next, virtual screening integrating multiple PI3Kγ protein structures was demonstrated to significantly improve the screening enrichment rate comparing to that with an individual protein structure. Last, a multi-conformational Naïve Bayesian Classification model with the optimal docking programs was constructed, and it performed a true capability in the screening of PI3Kγ inhibitors. Taken together, the current study could provide some guidance for the docking-based virtual screening to discover novel PI3Kγ inhibitors.

Integrated molecular modeling techniques to reveal selective mechanisms of inhibitors to PI3Kδ with marketed Idelalisib

Phosphatidylinositol-3-kinase (PI3K) is important for cell proliferation, differentiation, and apoptosis, and the diverse physiological roles of different PI3K isoforms have highlighted the significance of the development of PI3Kδ inhibitors. A large number of PI3Kδ inhibitors have been reported after the FDA approval of Idelalisib, but the clinical use of Idelalisib was limited because of its serious side effects. Therefore, great efforts have been made on the development of PI3Kδ inhibitors with higher selectivity and lower toxicity, but there is no new PI3Kδ inhibitor coming into the market so far. Even so, as the first listed PI3K inhibitor, Idelalisib could be used as an effective tool to investigate the selective inhibition mechanism of PI3Kδ. Thus, in this study, a modeling strategy integrated 3D-QSAR, pharmacophore model, and molecular dynamics simulation was employed to reveal the key chemical characteristics of Idelalisib analogs and the binding pattern between the inhibitors and PI3Kδ. First, the CoMFA model with high statistical significance was built to reveal the general structure-activity relationships. And then, a reliable pharmacophore model with a robust discrimination capability was constructed to expound the main chemical characteristics of the PI3Kδ inhibitors. Finally, molecular dynamics simulation was conducted to explore the binding modes and some key residues refer to δ-selective binding were highlighted with binding-free energy calculation. In summary, these models and results would provide some effective help for the discovery or the rational design of novel PI3Kδ inhibitors.